Visual and audio paths do not have the same learning speed. Even without the additional convolutional layers comprised in FiLM, the training of the audio path needs more iterations than for the visual training. Inspired from [1]}, we investigate a new multimodal training technique by randomly dropping the update of the visual weights to allow the audio path to train longer. In Fig. REF , we report the accuracy in function of the dropping rate of the weight update of the visual path. Dropping too often the visual path decreases results compared to training without dropping. We suppose that the visual path is not trained enough. It is also observed that not dropping enough the visual path gives also slightly poorer results. We suppose that the network can not exploit enough sound information. Furthermore, it looses visual information as the visual path is not trained as much as needed.
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